Role of Machine Learning in IoT-Cloud Enabled Healthcare

Prospects, Challenges and Opportunities
1. Auflage 2027
ISBN: 978-1-041-31079-2
Verlag: Taylor & Francis Ltd

Buch, Englisch, 338 Seiten, Format (B × H): 156 mm x 234 mm

Today, Machine Learning (ML) plays a transformative role in IoT-cloud enabled healthcare by enabling intelligent analysis of real-time patient data collected through wearable devices, sensors, and smart medical systems. ML models support disease prediction, remote patient monitoring, personalized treatment, anomaly detection, and clinical decision-making. Cloud platforms provide scalable storage, processing power, and seamless accessibility for healthcare data analytics. However, some challenges remain like data privacy, security, interoperability, data quality, and regulatory compliance remain significant. Some of the future opportunities arising from this convergence include federated learning, explainable AI, digital twins, predictive healthcare, and AI-driven precision medicine – which have the potential to improve healthcare efficiency and patient outcomes.

Zielgruppe

Academic, Postgraduate, Undergraduate Advanced, and Undergraduate Core

Autoren/Hrsg.

Tyagi, Amit Kumar

Fachgebiete

• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

Preface. Acknowledgement. NephroPredict: Multi-Algorithm Detection of Chronic Kidney Disease. Supervised Learning Techniques for IoT Data Analytics. Transforming Healthcare with Early Disease Detection Using IoT, Cloud, and Machine Learning. Multifunctional Role of Internet of Medical Things (IoMT) for Smart Hospitals: A Bibliometric Review. Real Time Cardiovascular Disease Monitoring (RTCDM) through Integrated PCA-XGB and Cloud based ESP32 Microcontroller Framework. AI for Accident Detection and Emergency Response. Architectural Framework for IoT-Cloud Ecosystems. Brain Tumor Detection using Convolutional Neural Network. Machine Learning Approaches to Interstellar Phenomenon Classification and Analysis. Automated Screening of Diabetic Retinopathy Using Explainable AI with Grad-CAM. A Hybrid Deep Learning Approach for Early Lung Cancer Detection Using CT Images. An AI-Driven Approach for Enhancing Patient Healthcare in Resource-Constrained Rural Areas. Design and Development of a Model for Heart Disease Prediction using Machine Learning in Healthcare. Foundations and Challenges of Deep Learning in Brain Tumor Detection. Interoperability and Scalability Challenges. Real-Time Medical Image Analysis for Multi-Cancer Screening using Optimized Extreme Learning Machines. COVID-19 and Small & Medium Enterprises: Disruptions, Resilience, and Digital Transformation. Index.

Über Autor(innen)

Amit Kumar Tyagi is working as an Assistant Professor, at National Forensic Sciences University, Gandhinagar, Gujarat, India. He received his Ph.D. Degree (Full-Time) in 2018 from Pondicherry Central University, India. Regarding his academic experience, he has worked as an assistant professor at several institutes like Lord Krishna College of Engineering (LKCE), Ghaziabad (for the periods of July 2009–July 2010, and October 2012–October 2013), Lingaya's Vidyapeeth (formerly known as Lingaya's University), Faridabad (September 2018–May 2019), VIT Chennai (June 2019–November 2022) and NIFT New Delhi (November 2022–September 2025). His current research focuses on Next Generation Machine Based Communications, Blockchain Technology, Smart and Secure Computing and Privacy. He is also a senior member of IEEE.